

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
	from fairscale.optim import OSS


__snake_case				:      Any   		=			logging.get_logger(__name__)

__snake_case				:      Any   		=			{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}



class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase=None  ,      _lowercase=None  ,      *_lowercase  ,      **_lowercase    )     -> List[str]:
									super().__init__(*_lowercase  ,      **_lowercase    )

									if config is None:
												assert isinstance(self.model  ,      _lowercase    ), (
												    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
												    F''' {self.model.__class__}'''
												)
												a_						:						List[Any] 							= self.model.config
									else:
												a_						:						int 							= config

									a_						:						List[str] 							= data_args
									a_						:						Union[str, Any] 							= self.config.tgt_vocab_size if isinstance(self.config  ,      _lowercase    ) else self.config.vocab_size

									if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
												assert self.config.pad_token_id is not None, (
												    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
												    " calculation or doing label smoothing."
												)

									if self.config.pad_token_id is None and self.config.eos_token_id is not None:
												logger.warning(
												    F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
												    """ padding.."""    )

									if self.args.label_smoothing == 0:
												a_						:						Any 							= torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id    )
									else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												a_						:						Optional[Any] 							= label_smoothed_nll_loss





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Dict:
									if self.optimizer is None:
												a_						:						Union[str, Any] 							= ["""bias""", """LayerNorm.weight"""]
												a_						:						Tuple 							= [
												    {
												        """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay    )],
												        """weight_decay""": self.args.weight_decay,
												    },
												    {
												        """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay    )],
												        """weight_decay""": 0.0,
												    },
												]
												a_						:						Tuple 							= Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
															a_						:						int 							= Adafactor
															a_						:						str 							= {"""scale_parameter""": False, """relative_step""": False}
												else:
															a_						:						int 							= AdamW
															a_						:						Union[str, Any] 							= {
															    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
															    """eps""": self.args.adam_epsilon,
															}
												a_						:						List[str] 							= self.args.learning_rate
												if self.sharded_ddp:
															a_						:						List[str] 							= OSS(
															    params=_lowercase  ,      optim=_lowercase  ,      **_lowercase  ,      )
												else:
															a_						:						Dict 							= optimizer_cls(_lowercase  ,      **_lowercase    )

									if self.lr_scheduler is None:
												a_						:						List[str] 							= self._get_lr_scheduler(_lowercase    )
									else:  # ignoring --lr_scheduler
												logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Dict:
									a_						:						Union[str, Any] 							= arg_to_scheduler[self.args.lr_scheduler]
									if self.args.lr_scheduler == "constant":
												a_						:						Any 							= schedule_func(self.optimizer    )
									elif self.args.lr_scheduler == "constant_w_warmup":
												a_						:						Dict 							= schedule_func(self.optimizer  ,      num_warmup_steps=self.args.warmup_steps    )
									else:
												a_						:						Any 							= schedule_func(
												    self.optimizer  ,      num_warmup_steps=self.args.warmup_steps  ,      num_training_steps=_lowercase    )
									return scheduler





						def 			UpperCamelCase__					(    self    )     -> Optional[torch.utils.data.Sampler]:
									if isinstance(self.train_dataset  ,      torch.utils.data.IterableDataset    ):
												return None
									elif is_torch_tpu_available():
												return get_tpu_sampler(self.train_dataset    )
									else:
												if self.args.sortish_sampler:
															self.train_dataset.make_sortish_sampler(
															    self.args.per_device_train_batch_size  ,      distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)  ,      )

												return (
												    RandomSampler(self.train_dataset    )
												    if self.args.local_rank == -1
												    else DistributedSampler(self.train_dataset    )
												)





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> Dict:
									if self.args.label_smoothing == 0:
												if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
															# force training to ignore pad token
															a_						:						Tuple 							= model(**_lowercase  ,      use_cache=_lowercase    )[0]
															a_						:						int 							= self.loss_fn(logits.view(-1  ,      logits.shape[-1]    )  ,      labels.view(-1    )    )
												else:
															# compute usual loss via models
															a_							,					a_						:						Tuple 							= model(**_lowercase  ,      labels=_lowercase  ,      use_cache=_lowercase    )[:2]
									else:
												# compute label smoothed loss
												a_						:						List[Any] 							= model(**_lowercase  ,      use_cache=_lowercase    )[0]
												a_						:						List[str] 							= torch.nn.functional.log_softmax(_lowercase  ,      dim=-1    )
												a_							,					a_						:						List[str] 							= self.loss_fn(_lowercase  ,      _lowercase  ,      self.args.label_smoothing  ,      ignore_index=self.config.pad_token_id    )
									return loss, logits





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> Optional[Any]:
									a_						:						List[str] 							= inputs.pop("""labels"""    )
									a_							,					a_						:						Any 							= self._compute_loss(_lowercase  ,      _lowercase  ,      _lowercase    )
									return loss





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      )     -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
									a_						:						Any 							= self._prepare_inputs(_lowercase    )

									a_						:						int 							= {
									    """max_length""": self.data_args.val_max_target_length
									    if self.data_args is not None
									    else self.config.max_length,
									    """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
									}

									if self.args.predict_with_generate and not self.args.prediction_loss_only:
												a_						:						str 							= self.model.generate(
												    inputs["""input_ids"""]  ,      attention_mask=inputs["""attention_mask"""]  ,      **_lowercase  ,      )
												# in case the batch is shorter than max length, the output should be padded
												if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
															a_						:						Optional[int] 							= self._pad_tensors_to_max_len(_lowercase  ,      gen_kwargs["""max_length"""]    )

									a_						:						Tuple 							= inputs.pop("""labels"""    )
									with torch.no_grad():
												# compute loss on predict data
												a_							,					a_						:						List[Any] 							= self._compute_loss(_lowercase  ,      _lowercase  ,      _lowercase    )

									a_						:						Any 							= loss.mean().detach()
									if self.args.prediction_loss_only:
												return (loss, None, None)

									a_						:						int 							= generated_tokens if self.args.predict_with_generate else logits

									if labels.shape[-1] < gen_kwargs["max_length"]:
												a_						:						List[str] 							= self._pad_tensors_to_max_len(_lowercase  ,      gen_kwargs["""max_length"""]    )

									return (loss, logits, labels)





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> Union[str, Any]:
									# If PAD token is not defined at least EOS token has to be defined
									a_						:						Optional[int] 							= self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

									if pad_token_id is None:
												raise ValueError(
												    """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
												    F''' padded to `max_length`={max_length}'''    )

									a_						:						Optional[Any] 							= pad_token_id * torch.ones(
									    (tensor.shape[0], max_length)  ,      dtype=tensor.dtype  ,      device=tensor.device    )
									a_						:						Union[str, Any] 							= tensor
									return padded_tensor





from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


